Anomaly detection
In this section, you’ll learn how an anomaly can be detected based on the metrics you have gathered and can be the basis for rules starting from the SLIs.
Anomalies can be inferred from the following:
- Simple indicator deterministic cases
- Multiple indicators deterministic cases
- Time series analysis
Let’s dig into these categories to explain how the observability metrics can be leveraged to find out the root cause and, in fine, new points of attention and rules for your data sources.
Simple indicator deterministic cases
Anomalies can be detected by adding a series of basic checks to the rules based on the type of metrics you gather, as well as the business logic.
By handling missing values effectively, organizations can prevent potential misinterpretations or errors in data analysis. For example, if the data producer or consumer expects no missing values in the data source, a deterministic rule addressing the number of...
